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POST-DOCTORAT : Data Scientist pour projet de recherche sur la détection des troubles neurovisuels en conduite automobile

Offre en lien avec l’Action/le Réseau : – — –/Innovation

Laboratoire/Entreprise : Laboratoire LISV
Durée : 10 mois
Contact : olivier.rabreau@uvsq.fr
Date limite de publication : 2025-06-01

Contexte :
Notre laboratoire de recherche (www.lisv.uvsq.fr) se consacre à l’exploration de nouvelles méthodes pour détecter les troubles neurovisuels, en particulier chez les patients ayant subi un AVC. Dans le cadre de notre projet de recherche “APTICONDUITE”, financé par la Délégation de la Sécurité Routière (DSR), nous utilisons un simulateur de conduite automobile équipé de capteurs biométriques afin de recueillir des données comportementales et physiologiques.

Nous recherchons un(e) Data Scientist motivé(e) pour analyser ces données et contribuer au développement d’une méthodologie innovante permettant d’identifier des dysfonctionnements neurovisuels.

L’objectif étant de pouvoir proposer une meilleure solution d’accompagnement du diagnostic basée sur la classification de résultats de tests d’aptitude réalisés via le simulateur.

Contexte de travail

• Le ou la candidat(e) rejoindra une équipe de chercheurs dynamique et en pleine expansion au sein du laboratoire LISV de l’Université de Versailles Saint-Quentin (www.lisv.uvsq.fr). Le laboratoire est membre de l’université Paris-Saclay. Le travail sera effectué dans l’équipe “Robotique interactive” coordonnée par le Pr. Abderraouf Benali qui explore l’interaction entre l’utilisateur et les systèmes robotiques au sein de son environnement.

• L’étude s’inscrit dans le cadre du projet de recherche “APTICONDUITE” financé par la Délégation de la Sécurité Routière (DSR). Ce projet est en collaboration avec le centre national d’expertise sur les aides à la mobilité, le CEREMH (www.ceremh.org) et le centre hospitalier de Plaisir (www.ch-plaisir.fr).

Conditions

• Rémunération brute : 3036.81€/mois
• Durée : 10 mois avec possibilité de prolongation selon l’avancée du projet.
• Lieu de travail : laboratoire LISV, 10-12 avenue de l’Europe, 78140 Vélizy (France) (poste en présentiel avec possibilité de télétravail)

Sujet :
Missions

• Traiter et analyser les données recueillies via des capteurs biométriques (ECG, suivi oculaire, EEG, etc.) lors des sessions de simulation de conduite.
• Mettre en œuvre des techniques d’apprentissage automatique (Machine Learning) pour identifier des patterns liés aux troubles neurovisuels.
• Collaborer avec une équipe de chercheurs pluridisciplinaires (neurologues, ingénieurs, psychologues).
• Développer et valider des modèles prédictifs afin de détecter les anomalies neurovisuelles chez les patients.
• Participer à la rédaction de rapports scientifiques et à la communication des résultats lors de conférences ou de publications.

Profil du candidat :
• Formation : Doctorat en Science des Données, Informatique, Mathématiques Appliquées, ou domaine connexe.
• Compétences techniques :
o Maîtrise des techniques de Machine Learning, de classification automatique et de tests de significativité.
o Maîtrise des langages de programmation pour le traitement des données.
o Expérience dans le traitement de données biométriques, physiologiques ou médicales est un plus.
o Connaissance des outils de traitement de signaux (EEG, ECG, suivi oculaire) est un plus.
o Maîtrise des bibliothèques telles que TensorFlow, PyTorch, Scikit-learn, etc.
• Compétences analytiques : Capacité à interpréter des données complexes et à proposer des solutions méthodologiques adaptées.
• Qualités : Autonomie, rigueur scientifique, esprit d’équipe et curiosité pour les applications médicales et les neurosciences.

Formation et compétences requises :
• Formation : Doctorat en Science des Données, Informatique, Mathématiques Appliquées, ou domaine connexe.
• Compétences techniques :
o Maîtrise des techniques de Machine Learning, de classification automatique et de tests de significativité.
o Maîtrise des langages de programmation pour le traitement des données.
o Expérience dans le traitement de données biométriques, physiologiques ou médicales est un plus.
o Connaissance des outils de traitement de signaux (EEG, ECG, suivi oculaire) est un plus.
o Maîtrise des bibliothèques telles que TensorFlow, PyTorch, Scikit-learn, etc.
• Compétences analytiques : Capacité à interpréter des données complexes et à proposer des solutions méthodologiques adaptées.
• Qualités : Autonomie, rigueur scientifique, esprit d’équipe et curiosité pour les applications médicales et les neurosciences.

Adresse d’emploi :
Laboratoire LISV, 10-12 avenue de l’Europe, 78140 Vélizy, France

Document attaché : 202409200755_PostV9_French.pdf

45th IEEE International Conference on Distributed Computing Systems (IEEE ICDCS 2025)

Date : 2025-07-20 => 2025-07-23
Lieu : Glasgow, Scotland, UK

SCOPE

The 45th IEEE International Conference on Distributed Computing Systems (IEEE ICDCS 2025) is the premier international forum for researchers and practitioners to present, discuss and exchange cutting edge ideas as well as the latest findings on topics related to all aspects of distributed computing systems. The conference will be held over July 20-23rd, 2025 in Glasgow, Scotland, UK. We invite you to submit original contributions to IEEE ICDCS 2025 (https://icdcs2025.icdcs.org/)

Papers can be submitted to one of the following tracks:

* Cloud Computing
* Distributed Algorithms
* Big Data, Models & Systems
* Distributed Fault Tolerance
* Distributed OS and Middleware
* Edge Computing
* IoT + CPS
* Mobile Computing
* Security, Privacy and Trust
* Blockchains/Databases
* Distributed Systems for AI/ML
* AI/ML for Distributed Systems
* Deployed/Emergent Applications & Infrastructures

PAPER SUBMISSION

The Paper strand provides the opportunity for researchers to present their new state-of-the-art research in Distributed Computing Systems, which makes, or has the potential to make, a significant contribution to the field.
Papers must be original and unpublished and must not be submitted concurrently for publication elsewhere. All paper submissions should follow the IEEE 8.5” x 11” two-column format using 10pt fonts and the IEEE Conference template (downloadable by selecting “Conferences” in the IEEE-Template Selector https://template-selector.ieee.org/).
Each submission can have up to eleven (11) pages (including figures, tables, appendices, and references).
Papers exceeding this page limit or with smaller fonts will be desk-rejected without review.

The paper review process is double-blind. Authors are required to take all reasonable steps to preserve the anonymity of their submission.
The submission must not include author information and must not include citations or discussion of related work that would make the authorship apparent. While authors can upload their paper to institutional or other preprint repositories such as arXiv.org before reviewing is complete, we generally discourage this since it places anonymity at risk.
If authors decide to upload their paper to a preprint site, they must make sure that the title and abstract of their submission to
ICDCS are different from the title and abstract of the preprint version, so that it is not immediately
obvious that the two versions are by the same authors and with the same content.
To encourage reproducibility, we encourage the authors, whenever it is possible, to include in their paper a link to an anonymised GitHub repository with all source code, scripts and data needed for the reproduction of their results.

For each accepted paper, at least one author is required to pay a full author registration and attend the conference in-person to present their work on-site. Any no-show papers will be reported to the publisher and removed from the conference proceedings. For authors with multiple papers accepted by the conference, a separate author registration is required for each paper. The authors should adhere to ethic and professional standards of IEEE. Please refer to IEEE Code of Ethics and IEEE Policy of AI-Generated Text.

Note: A set of highly selected IEEE ICDCS 2025 papers will be considered for publication in the IEEE Transactions on Parallel and Distributed Systems (TPDS). The selected papers will undergo an extension process, transforming them from their initial IEEE ICDCS conference format. These extended versions will then be subject to a review by an editor from IEEE TPDS.

*** Paper Deadline Dates – Time zone: Anywhere on Earth (AoE) ***
*Paper submission website: https://easychair.org/conferences/?conf=icdcs2025
*Paper Abstract Registration: 4 December 2024
*Paper Submission Due: 11 December 2024
*Author Notification: 2 April 2025
*Camera-Ready Submission: 16 April 2025

For inquiries regarding the Call for Papers, email: icdcs2025-FPTracks@easychair.org

*General Chairs
Christos Anagnostopoulos, University of Glasgow, UK
Iadh Ounis, University of Glasgow, UK

*Program Chairs
Songqing Chen, George Mason University, USA
Neeraj Suri, Lancaster University, UK

Lien direct

Notre site web : www.madics.fr
Suivez-nous sur Tweeter : @GDR_MADICS
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XAI to satisfy safety requirements of B5G V2X infrastructure

Offre en lien avec l’Action/le Réseau : HELP/Doctorants

Laboratoire/Entreprise : CERI SN
Durée : 36 mois
Contact : jerry.lonlac@imt-nord-europe.fr
Date limite de publication : 2024-10-31

Contexte :
Public establishment belonging to IMT (Institut Mines-Télécom), placed under the supervision of the Ministry of Industry, IMT Nord Europe has three main objectives: providing our students with ethically responsible engineering practice enabling them to solve 21st century issues, carrying out our R&D activities leading to
outstanding innovations and supporting territorial development through innovation and entrepreneurship.
Ideally positioned at the heart of Europe, 1 hour away from Paris, 30 min from Brussels and 1h30 from London, IMT Nord Europe has strong ambitions to become a main actor of the current industrial transitions, digital and environmental, by combining education and research on engineering and digital technologies. Located on two main campuses dedicated to research and education in Douai and Lille, IMT Nord Europe offers research facilities of almost 20,000m² in the following areas:
– Digital science,
– Processes for industry and services,
– Energy and Environment,
– Materials and Processes.
For more details, visit the School’s website: www.imt-nord-europe.fr

The position is vacant within the Centre for Education, Research and Innovation (CERI) Digital Systems. It covers a wide disciplinary field linked to constrained systems (the Internet of Objects, robotics), Humans (and in particular their interactions with the digital world) or even complex systems through the prism of Artificial Intelligence and Automation. The 34 lecturer-researchers and 6 engineers at CERI are able to cover all teaching fields in the field of digital sciences and technologies (Data, Artificial Intelligence, Telecoms, Networks, Systems, Applications, Cybersecurity, etc.). It is structured around 3 research groups: ARTS (Autonomous Resilient Systems), HIDE (Human, Interaction, DEcision) and McLEOD (Modelling and Control of Complex
systems in Large Environments requiring Optimized Decision).

Sujet :
The thesis will be carried out within the framework of the ANR “TRAVEL” project, which aims to propose an eXplainable Artificial Intelligence (XAI) framework to explain the logic behind the black-box model behaviors trained on data related to vehicular communications (V2X) and allowing to improve the communication
network infrastructure at various levels (PHY, SDN, and NFV), thus ensuring a safe and efficient deployment. The ICT infrastructure is becoming increasingly complex and interdependent due to rapid virtualization, softwarization, data massification, and cloudification. With the widespread deployment of wireless networks, intelligent and automated network operation is becoming increasingly essential, deserving tremendous research effort. AI holds significant potential for application in the network field (AI for IT operations), promising improvements in operational efficiency, Quality of Service (QoS), and Quality of Experience (QoE), along with reductions in operational costs and complexity [1]. Achieving network self-maintenance and self-healing capabilities is also a major concern. This entails effectively integrating cross-layer anomaly detection, root cause analysis, explainability, and response into a closed-control loop, guided by the output of root cause analysis and predefined policies to restore system performance. This necessity for intelligent network operation coincides with the ongoing evolution of cellular technologies, notably the progression from 5G to what is anticipated as 6G.

Despite the excellent performance of AI models on enormous tasks in V2X Infrastructure, when their decisions cannot be well-interpreted, it is difficult to trust them. In recent years, the proliferation of AI applications in network communications and cybersecurity with the requirements of the European Commission for algorithms to provide explanations to users has reinforced the necessity of employing XAI in this field. Indeed, the advent of 5G specifically carries the ambition of a very wide coverage, including outside cities. Combined with paradigms such as Software Defined Networking (SDN) and Network Function Virtualization (NFV), 5G is
expected to enable faster access and high scalability of both devices, services, applications and data, and thus eventually establish itself as the mobile communication system for all applications in the smart city, including V2X communications [2]. The thesis‘s works will be applied on the 5G Core Network (5G-CN) and its interfaces with the 5G-RAN. It aims to develop XAI approaches to network slicing automation at the interface between
the 5G-CN and the 5G-RAN [3] to allow a deployment of AI-assisted sliced networks in V2X infrastructure in a way that satisfies safety constraints. Indeed, V2X infrastructure is a critical domain which involves human lives, and in which any flaw may have dramatic consequences. Therefore, any malfunction must be anticipated, and anyhow completely auditable [4]. To achieve this goal, this thesis will develop XAI methods that rely on the theory in the domain of V2X infrastructure for providing better explanations. That will be made both during data collection and feature engineering phases. In fact, a scientific theory represents a well-founded and widely accepted statement, hypothesis, or explanation that has withstood rigorous testing and scrutiny [5].
We will also explore local interpretability techniques to explain local inference of AI models regarding V2X infrastructure safety requirement parameters by providing alternative or counterfactual scenarios for the explained scenarios. Those techniques will help us for each scenario to explain, find its most similar scenario measured by a chosen distance metric, but that has an opposite AI inference.

Profil du candidat :
The objectives of this thesis are:
● Explore the current state-of-the-art XAI approaches in the field of V2X infrastructure.
● Develop XAI schemes based on existing theory in the field of V2X infrastructure
● Integrate V2X infrastructure and application safety requirements into the XAI architecture.

Formation et compétences requises :
● M.Sc. degree (or equivalent) in Computer science or related discipline,
● Strong background in Artificial Intelligence/Machine Learning with, if possible, experience in eXplainable Artificial Intelligence
● Experience in the field of communication networks would be an undeniable advantage
● Good programming skills (Python, Java, C++),
● Good written and communication skills in English.
● Ability to organize and manage priorities in order to meet deadlines

Adresse d’emploi :
Cité scientifique
Rue Guglielmo Marconi,
59650 Villeneuve-d’Ascq
Lille, France

Document attaché : 202409160950_These_ANR_Travel_XAI for satisfy safety requirements-Final.pdf

Offre de Post-doc au LORIA – site de Metz

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LORIA
Durée : 2 ans
Contact : lydia.boudjeloud-assala@univ-lorraine.fr
Date limite de publication : 2024-11-12

Contexte :
Artificial Intelligence, with the advent of Deep Learning, has recently enabled spectacular advances in various fields of scientific research. It is now currently used in fields such as chemistry and molecular biology, astrophysics, particle physics, health science, etc. The aim of this post-doctoral position is to explore some possible applications of Deep Learning to materials science.

Sujet :
Post-doctoral position
Deep learning in materials science : predicting
macroscopic properties of a material by
analyzing its microscopic structure

Profil du candidat :
Mots-clés : machine learning, données image
Le candidat devra être titulaire d’un doctorat en Informatique.

Formation et compétences requises :

Adresse d’emploi :
LORIA – site de Metz
Ecole CentraleSupelec

Document attaché : 202409121054_MAMIENOVA_post-doc.pdf

Complex Event Processing in an AI System for Healthcare

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LS2N
Durée : 36 mois
Contact : guillaume.raschia@univ-nantes.fr
Date limite de publication : 2024-09-12

Contexte :
The PhD will take place in the European THCS “Transforming Health and Care Systems” project RENEW which means “Reshaping data-driven smart healthcare to optimize resources and personalize care for hypertensive patients through AI and digital twin models”. The RENEW project has started in June 2024 for 3 years long. It involves 9 partners from Romania, Suede, Switzerland, Poland, Italy, Slovenia and France. The LS2N partner leads the work package about the smart data processing, the personal profiles and digital twin design.

Sujet :
Health and well-being at home require to monitor in near-real time a bunch of measures and raw events at a large scale and a high frequency, coming both from the individuals and their environment. Focusing on hypertensive patients only, it is well-known that lifestyle (diet, physical activity, tobacco, alcohol, overweight) plays a crucial role in risk assessment.
Thus, the PhD aims at building, maintaining and analyzing digital twins for healthcare. As part of the RENEW project, the ultimate goal is to give feedback to individuals on their practices and lifestyle based on IA models and stream processing. Also, health institutions should be able to conduct real-time analyzes and gain insights from personal models of a large cohort of patients. All in all, it is then necessary to develop an online architecture capable of continuously collecting, preparing and analyzing health and care data from multiple sources.

Profil du candidat :

Formation et compétences requises :

Adresse d’emploi :
LS2N – site de Polytech Nantes
Rue Christian Pauc
44300 NANTES

Document attaché : 202409111132_Sujet-PhD-Renew.pdf

Statistical and Deep Learning Schemes for Maritime RADAR Detection and Surveillance

Offre en lien avec l’Action/le Réseau : – — –/Doctorants

Laboratoire/Entreprise : SONDRA, L2S, CentraleSupelec
Durée : 18 mois
Contact : chengfang.ren@centralesupelec.fr
Date limite de publication : 2025-01-01

Contexte :
Coastal RADAR aims to control and monitor the maritime surface. By matching transmitted and received electromagnetic waves, radar is able to detect and range vessels whose back-scattered a sufficiently strong signal relative to the sea clutter. The detection performance generally depends on the target’s Radar Cross Section (RCS) and the clutter noise power, which can be summarized by the Signal to Noise Ratio (SNR). In a rough sea state (e.g., 5 on the Douglas sea scale), the detection performance of small vessels hidden by strong sea clutter (Bragg clutter) can deteriorate drastically. The postdoc aims to innovate and improve detection methods previously developed in SONDRA and L2S laboratory in this context.

Sujet :
The postdoc will first investigate robust detection methods such as Adaptive Normalized Matched Filters (ANMF) [1], which require estimating the covariance matrix of secondary data in a robust manner [2]. The covariance matrix estimation step could include prior information on the structure using either Riemannian geometry [3] or optimization under persymmetric [4], Toeplitz [5], Kronecker constraints [6], etc. This step could be crucial for improving detection and mitigating the probability
of false alarms. The second direction investigates deep learning approaches to handle a detection, segmentation or/and generation scheme, either end-to-end or in an unrolling way [7, 8]. The latter approach can be less data-hungry and easier to interpret. Since radar data are complex-valued, an architecture based on Complex-Valued Neural Networks (CVNN) [9] can be exploited to learn radar phase information. Meta-learning methods can be investigated to improve detection performance. The developed algorithms will be tested on CSIR database and maritime data collected by our partner BOWEN.
This position is fully funded by ANR ASTRID Maturation for 18 months.

Profil du candidat :
We seek a highly motivated postdoctoral fellow to investigate statistical and deep learning methods for detection in Radar. The ideal candidate should possess the following qualifications:
• A robust background in machine learning, signal processing, or applied mathematics (statistics,
optimization, etc.).
• Strong programming abilities in either Matlab or Python.

Formation et compétences requises :

Adresse d’emploi :
Centralesupélec, 3 Rue Joliot Curie, 91190 Gif-sur-Yvette, France

Document attaché : 202409101551_Postdoc_BOWEN.pdf

Conferences around Optimization at CNRS headquarters October 3 and 4 and Journée Industrielle du GDR ROD et RT Optimisation October 2 (at Sorbonne Uni

Date : 2024-10-03 => 2024-10-02
Lieu : CNRS headquartes, Paris 16

Dear colleagues,

It is with great pleasure that we invite you to three exciting events around “Optimization”, focus theme for CNRS Sciences informatiques in 2024.
–> more information about the events and links to further activities are available at https://www.ins2i.cnrs.fr/fr/loptimisation-au-coeur-des-defis-des-sciences-informatiques
–> Default language for the October 2 and 3 events is French, while English will be default language on October 4
–> Spots are limited for all three events and will be given on a first come, first served basis. In all fairness (and to avoid food waste), please register only if you are sure to attend (and keep us informed if you have to cancel your participation)

(1) October 2: Journée Industrielle du GDR ROD et RT Optimisation (Sorbonne Université – Jussieu – Paris 5ième)
Keynotes by Olivier Juan (EDF R&D), Tristan Rigaut (Schneider Electric), Jean-Charles Billaut (LIFAT, Université de Tours), Adam Ouorou (Orange Labs), Alexandre Marié (Artelys), Gautier Avril (Purecontrol)
–> Further information and registration: http://gdrro.lip6.fr/?q=node/334

(2) October 3: L’optimisation : au cœur des défis des sciences informatiques (CNRS headquarter – Paris 16ième)
The big conference for the larger public on October 3 at the CNRS headquarter (Paris 16ième)
Keynotes: Jérôme BOLTE, Claire MATHIEU, Axel PARMENTIER, and Gabriel PEYRÉ
Flash presentations by: Simon APERS, Céline COMTE, Sophie HUIBERTS, Martin KREJCA, Clément W. ROYER, David SAULPIC, Sandra ULRICH-NGUEVEU and 3 industrial speakers
–> Further information and registration: https://www.ins2i.cnrs.fr/fr/cnrsinfo/conference-loptimisation-au-coeur-des-defis-des-sciences-informatiques

(3) October 4: Scientific conference (CNRS headquarter – Paris 16ième)
In cooperation with 9 GDRs of CNRS Sciences informatiques and the thematic network “optimization” of CNRS Mathématiques, we’ll come together to discuss the role of optimization in the various sub-disciplines
Keynote: Jean-Bernard LASSERRE
Presentations for the GDRs: BIMM (Annie Chateau), GPL (Clément Quinton), IASIS (Laure Blanc-Féraud), IFM (speaker to be confirmed), IG-RV (Julie Digne), MaDICS (Laure Berti-Equille), RADIA (Christophe Lecoutre), ROD (Michaël Poss), RSD (Christelle Caillouet), RT optimisation (Jean-Baptiste Caillaux)
The presentations will be complemented by discussions around the optimization landscape in France (current situation and our ambition) and breakouts around (i) industrial vs. academic research (ii) ecological aspects of optimization, (iii) optimization and ethics
–> Further information and registration: https://www.ins2i.cnrs.fr/en/scientific-day-optimization

We look forward to welcoming you at these events.

Kind regards,
the scientific organizing committee:

Claudia D’Ambrosio, Carola Doerr, Jérôme Malick, Alantha Newmann, Edouard Pauwels

Lien direct

Notre site web : www.madics.fr
Suivez-nous sur Tweeter : @GDR_MADICS
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Ecole IA2 : Intelligence Artificielle et Démocratie

Date : 2024-10-14 => 2024-10-18
Lieu : Sophia Antipolis

Cette édition IA2 2024 concerne le rôle de l’intelligence artificielle en tant que facteur déterminant pour l’avenir de l’humanité, car elle contribue avec ses applications à transformer considérablement la vie des individus et à avoir un impact sur les communautés humaines. Plus en particulier, cette école a pour objectif de traiter les questions suivantes : Quel rôle l’IA joue-t-elle dans les relations entre le citoyen et l’administration ou le gouvernement ? Qui utilise cette technologie et dans quel but ? Comment l’utilisation de l’IA influence-t-elle les relations de pouvoir dans l’élaboration des politiques et la confiance des citoyens dans les institutions démocratiques ?

Lien direct

Notre site web : www.madics.fr
Suivez-nous sur Tweeter : @GDR_MADICS
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Postdoctoral Research at ILLS – MILA : Reinforcement Learning for Robust Decision-Making and Dispatcher Assistance in Power Grids

Offre en lien avec l’Action/le Réseau : DOING/Doctorants

Laboratoire/Entreprise : IRL2020 – ILLS (International Laboratory on Learni
Durée : 18 months
Contact : pablo.piantanida@mila.quebec
Date limite de publication : 2024-11-30

Contexte :

Sujet :
Bonjour (English below),

Offre de post-doc de 18 mois au ILLS – MILA, McGill – ETS – CNRS – CentraleSupélec – Université Paris Saclay

Titre : Reinforcement Learning for Robust Decision-Making and Dispatcher Assistance in Power Grids
Date de prise de poste souhaitée : October – November 2024

Candidature à envoyer avant le 20/09/2024 à :
pablo.piantanida@mila.quebec

L’offre détaillée est jointe à ce message.

Bien cordialement,
Pablo Piantanida

——

Hello,

18-month Post-doc Opportunity at ILLS – MILA, McGill – ETS – CNRS – CentraleSupélec – Université Paris Saclay

Title: Reinforcement Learning for Robust Decision-Making and Dispatcher Assistance in Power Grids
Preferred Start Date: October – November 2024

Applications should be sent before 09/20/2024 to:
pablo.piantanida@mila.quebec

The detailed offer is attached to this email.

Best regards,
Pablo Piantanida

Profil du candidat :

Formation et compétences requises :

Adresse d’emploi :
Montréal, QC, Canada

Document attaché : 202409060152_Postdoc_RTE_LaJavaness_ILLS.pdf